Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
Source abstract
Abstract
Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Advanced Manufacturing and Logistics Optimization · Assembly Line Balancing Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130113, China
Youibot Robotics Co., Ltd., Shenzhen 518112, China
Funding
National Key Research and Development Program of China
References (25)
Show source-provided references
1. Decentral task allocation for industrial AGV-systems with routing constraints10.1016/j.jmsy.2021.11.012 ↗
2. A fault-tolerant control strategy for multiple automated guided vehicles10.1016/j.jmsy.2020.02.009 ↗
3. Time-space network model and MILP formulation of the conflict-free routing problem of a capacitated AGV system10.1016/j.cie.2020.106270 ↗
4. Heger, J., and Voss, T. (2018, January 18–20). Dynamic priority based dispatching of AGVs in flexible job shops. Proceedings of the 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Gulf of Naples, Italy.
5. Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems10.1016/j.cie.2015.10.017 ↗
6. A multi-agent based approach to dynamic scheduling with flexible processing capabilities10.1007/s10845-015-1069-x ↗
7. Xue, T., Zeng, P., and Yu, H. (2018, January 20–22). A reinforcement learning method for multi-AGV scheduling in manufacturing. Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France.10.1109/ICIT.2018.8352413 ↗
8. A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method10.1109/TIE.2021.3097668 ↗
9. A Relative State of Health Estimation Method Based on Wavelet Analysis for Lithium-Ion Battery Cells10.1109/TIE.2020.3001836 ↗
10. Resource management in decentralized industrial Automated Guided Vehicle systems
11. Survey of research in the design and control of automated guided vehicle systems10.1016/j.ejor.2004.09.020 ↗
12. A review of design and control of automated guided vehicle systems10.1016/j.ejor.2005.01.036 ↗
13. Increasing manufacturing flexibility through battery management of automated guided vehicles10.1016/j.cie.2018.01.026 ↗
14. Time Windows Based Dynamic Routing in Multi-AGV Systems
15. Make more digital twins
16. Secure sharing of big digital twin data for smart manufacturing based on blockchain10.1016/j.jmsy.2021.09.014 ↗
17. Review of digital twin about concepts, technologies, and industrial applications10.1016/j.jmsy.2020.06.017 ↗
18. Digital-Twin-Based Job Shop Scheduling Toward Smart Manufacturing10.1109/TII.2019.2938572 ↗
19. Digital Twin Enhanced Dynamic Job-Shop Scheduling10.1016/j.jmsy.2020.04.008 ↗
20. A Review of the Roles of Digital Twin in CPS-based Production Systems10.1016/j.promfg.2017.07.198 ↗
21. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison10.1109/ACCESS.2018.2793265 ↗
22. Energy consumption analysis and prediction of electric vehicles based on real-world driving data10.1016/j.apenergy.2020.115408 ↗
23. An online state of charge estimation method with reduced prior battery testing information10.1016/j.ijepes.2014.06.017 ↗
24. A fast and elitist multiobjective genetic algorithm: NSGA-II10.1109/4235.996017 ↗
25. A genetic algorithm tutorial10.1007/BF00175354 ↗